for Research in Decision Analysis - GERAD (www.gerad.ca).
Machine learning and optimization research communities have been
increasingly gathering efforts to face hard global optimization problems.
For example, optimization is largely used for setting the parameters of
machine learning models, or to create more compact data representations
without much loss of information. In turn, machine learning is widely used
for data-driven optimization yielding methods better tailored for the final
practical applications.
The objective of this special issue is to publish innovative global
optimization algorithms revolving around the combination of optimization
and machine learning techniques. We encourage submissions on theory,
methods, and applications focusing on a broad range of optimization and
machine learning approaches in various domains.
The deadline for submissions is July 31, 2019. Papers will be peer-reviewed
according to the editorial policy of the Journal of Global Optimization.
They should be original, unpublished, and not currently under consideration
for publication elsewhere. Contributions should be prepared according to
the instructions to authors that can be found in the journal homepage.
Authors should upload their contributions using the submission site
https://www.editorialmanager.com/jogo/, by selecting the option SI:
GERAD-40.
You can read the call for papers here:
https://www.springer.com/mathematics/journal/10898/PSE?detailsPage=societies
Other inquiries should be sent directly to the Guest Editors in charge of
this issue:
Daniel Aloise, Polytechnique Montréal (daniel.aloise@gerad.ca),
Gilles Caporossi, HEC Montréal (gilles.caporossi@gerad.ca),
and
Sébastien Le Digabel, Polytechnique Montréal (sebastien.le.digabel@gerad.ca
).
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